code: def test_get_flavor_configuration_with_present_flavor_returns_expected_configuration(
      sklearn_knn_model, model_path
  ):
      mlflow.sklearn.save_model(
          sk_model=sklearn_knn_model, path=model_path
      )
  
      sklearn_flavor_config = (
          mlflow_model_utils._get_flavor_configuration(
              model_path=model_path,
              flavor_name=mlflow.sklearn.FLAVOR_NAME,
          )
      )
      model_config = Model.load(os.path.join(model_path, "MLmodel"))
      assert (
          sklearn_flavor_config
          == model_config.flavors[mlflow.sklearn.FLAVOR_NAME]
      )
  
file_path: mlflow/tests/utils/test_model_utils.py
imports: import os
  import sys
  import pytest
  import sklearn.neighbors
  from sklearn import datasets
  import mlflow.sklearn
  import mlflow.utils.model_utils
  from mlflow.exceptions import MlflowException
  from mlflow.mleap import FLAVOR_NAME
  from mlflow.models import Model
  from mlflow.protos.databricks_pb2 import RESOURCE_DOES_NOT_EXIST, ErrorCode
  from mlflow.utils.file_utils import TempDir
label_desc: Use image node_id 5 for calling a global function with example usage: test_get_flavor_configuration_with_present_flavor_returns_expected_configuration(sklearn_knn_model, model_path) without return types